Multichannel Wiener Filters in Binaural and Bilateral Hearing Aids — Speech Intelligibility Improvement and Robustness to DoA Errors

Kuklasinski, Adam; Jensen, Jesper

doi:10.17743/jaes.2016.0060

Cited by 14 publications

(14 citation statements)

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“…The expression forĜ PMKF (n, k) in (13) is similar to that of the SDW-MWF [11,21], which is written as…”

Section: Discussionmentioning

confidence: 99%

“…Examples exist in many geometries including linear and spherical arrays [1]. Multichannel speech enhancement methods generally exploit the spatial diversity between target and noise, and can be categorized as the beamforming based methods [2][3][4][5], post-filtering techniques [6][7][8][9], and multichannel Wiener filtering (MWF) [10][11][12][13]. For these conventional methods, the spatial correlation of the clean signals in different microphones is used to design the optimal filters, however, the temporal correlation of the speech signal is usually neglected.…”

Section: Introductionmentioning

confidence: 99%

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Modulation-Domain Parametric Multichannel Kalman Filtering for Speech Enhancement

Xue

Moore

Brookes

et al. 2018

2018 26th European Signal Processing Conference (EUSIPCO)

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The goal of speech enhancement is to reduce the noise signal while keeping the speech signal undistorted. Recently we developed the multichannel Kalman filtering (MKF) for speech enhancement, in which the temporal evolution of the speech signal and the spatial correlation between multichannel observations are jointly exploited to estimate the clean signal. In this paper, we extend the previous work to derive a parametric MKF (PMKF), which incorporates a controlling factor to achieve the trade-off between the speech distortion and noise reduction. The controlling factor weights between the speech distortion and noise reduction related terms in the cost function of PMKF, and based on the minimum mean squared error (MMSE) criterion, the optimal PMKF gain is derived. We analyse the performance of the proposed PMKF and show the differences with the speech distortion weighted multichannel Wiener filter (SDW-MWF). We conduct experiments in different noisy conditions to evaluate the impact of the controlling factor on the noise reduction performance, and the results demonstrate the effectiveness of the proposed method.

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“…The expression forĜ PMKF (n, k) in (13) is similar to that of the SDW-MWF [11,21], which is written as…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modulation-Domain Parametric Multichannel Kalman Filtering for Speech Enhancement

Xue

Moore

Brookes

et al. 2018

2018 26th European Signal Processing Conference (EUSIPCO)

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“…Given (35) and since Φ s ≥ 0, M − 1 > 0, andd H wdw > 0 (assuming thatd w = 0, i.e.,d = 0), we conclude that in the presence of RTF estimation errors δ ml r ≥ 0 and ξ ml r ≥ 0,…”

Section: A Impact Of Erroneous Rtfsmentioning

confidence: 99%

Analysis of Eigenvalue Decomposition-Based Late Reverberation Power Spectral Density Estimation

Kodrasi¹,

Doclo²

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Many speech dereverberation techniques require an estimate of the late reverberation power spectral density (PSD). State-of-the-art multi-channel methods for estimating the late reverberation PSD typically rely on 1) an estimate of the relative transfer functions (RTFs) of the target signal, 2) a model for the spatial coherence matrix of the late reverberation, and 3) an estimate of the reverberant speech or reverberant and noisy speech PSD matrix. The RTFs, the spatial coherence matrix, and the speech PSD matrix are all prone to modeling and estimation errors in practice, with the RTFs being particularly difficult to estimate accurately, especially in highly reverberant and noisy scenarios. Recently, we proposed an eigenvalue decomposition (EVD)-based late reverberation PSD estimator which does not require an estimate of the RTFs. In this paper, this EVD-based PSD estimator is further analyzed and its estimation accuracy and computational complexity is analytically compared to a state-of-the-art maximum likelihood (ML)-based PSD estimator. It is shown that for perfect knowledge of the RTFs, spatial coherence matrix, and reverberant speech PSD matrix, the ML-based and EVD-based PSD estimates are both equal to the true late reverberation PSD. In addition, it is shown that for erroneous RTFs but perfect knowledge of the spatial coherence matrix and reverberant speech PSD matrix, the MLbased PSD estimate is larger than or equal to the true late reverberation PSD, whereas the EVD-based PSD estimate is obviously still equal to the true late reverberation PSD. Finally, it is shown that when modeling and estimation errors occur in all quantities, the ML-based PSD estimate is larger than or equal to the EVD-based PSD estimate. Simulation results for several realistic acoustic scenarios demonstrate the advantages of using the EVD-based PSD estimator in a multi-channel Wiener filter, yielding a significantly better performance than the ML-based PSD estimator.

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“…In [19], This work was supported by the Oticon Foundation and NWO, the Dutch Organisation for Scientific Research. [26], [31], it was shown that the method in [15], [17] may lead to inaccurate estimates of the late reverberation PSD, when the early RATFs of the target include estimation errors. In [19], [26], a more accurate estimator for the late reverberation PSD was proposed, independent of early RATF estimation errors.…”

Section: Introductionmentioning

confidence: 99%

Robust Joint Estimation of Multimicrophone Signal Model Parameters

Koutrouvelis

Hendriks

Heusdens

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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One of the biggest challenges in multi-microphone applications is the estimation of the parameters of the signal model such as the power spectral densities (PSDs) of the sources, the early (relative) acoustic transfer functions of the sources with respect to the microphones, the PSD of late reverberation, and the PSDs of microphone-self noise. Typically, the existing methods estimate subsets of the aforementioned parameters and assume some of the other parameters to be known a priori. This may result in inconsistencies and inaccurately estimated parameters and potential performance degradation in the applications using these estimated parameters. So far, there is no method to jointly estimate all the aforementioned parameters. In this paper, we propose a robust method for jointly estimating all the aforementioned parameters using confirmatory factor analysis. The estimation accuracy of the signal-model parameters thus obtained outperforms existing methods in most cases. We experimentally show significant performance gains in several multi-microphone applications over state-of-the-art methods.

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Multichannel Wiener Filters in Binaural and Bilateral Hearing Aids — Speech Intelligibility Improvement and Robustness to DoA Errors

Cited by 14 publications

References 0 publications

Modulation-Domain Parametric Multichannel Kalman Filtering for Speech Enhancement

Modulation-Domain Parametric Multichannel Kalman Filtering for Speech Enhancement

Analysis of Eigenvalue Decomposition-Based Late Reverberation Power Spectral Density Estimation

Robust Joint Estimation of Multimicrophone Signal Model Parameters

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